Professor Nick Dulvy

Deep Blue Data

As overfishing, climate change and the myriad of associated complications come to bear on the world, Dulvy faces a unique challenge: how do we bring the plight of our ocean’s species, specifically sharks, to the surface?

To understand the threats, Dulvy looks to the case of the canary. Up until the 20th century, mining groups used canaries as a poisonous gas detection system. Coal miners navigated through dark tunnels with the bird, and if it went into distress, miners were alerted to toxic levels of carbon monoxide or methane in the passage. The canaries exposed a danger that was otherwise invisible to humans.

Like the precarious survival of the canary in a mine, declines in shark species populations send signals about environmental dangers plaguing our oceans. As co-chair of the International Union for Conservation of Nature (IUCN) Shark Specialist Group, Dulvy is using big data to reveal the current state of shark populations, and predict how shark numbers will change over the next century. His work is advancing knowledge and helping to protect the future of our oceans.

To untangle wicked problems like overfishing, researchers require new thinking and new models of collaboration. But most importantly, they must be equipped with the tools, training and expertise to translate massive amounts of data into knowledge and action. Dulvy is one of many people at SFU who are looking beyond the traditional boundaries of academic research to make real-world impact. Harnessing the power of big data to fuel discovery and provide evidence for change is key.

“Until we got to this place here and now, I would never have classified myself as a big data scientist,” Dulvy says.

“I think broadening the lens from this high-volume, high-velocity view of big data is critical. It opens up the audience to a much broader view about what big data means. For me, big data really means solving big problems.”

In 2014, Dulvy with IUCN's Shark Specialist Network evaluated the status of more than 1000 species of shark, ray, and chimaera species, and found that one quarter were threatened with extinction. They calculate that saving sharks will be 100 times more complex than saving elephants or rhinos.

“I think broadening the lens from this high-volume, high-velocity view of big data is critical. It opens up the audience to a much broader view about what big data means. For me, big data really means solving big problems.”

To investigate global populations and their risks, Dulvy collaborates with the IUCN Shark Specialist Group a network comprised of 136 researchers and policy specialists from over 30 nations across the world. His research combines “soft knowledge” drawn from sources ranging from gill dissections to libraries to oral narrative with large data sets from up-to-date species status reports from the IUCN Red List of Threatened Species.

“We're not dealing with high volume, high frequency data streams. Instead we're dealing with hard won data that we have to ensure the veracity of. Those processes can be slow, requiring peer-review; hence a species assessment can take a year and a half to complete. It may involve only tens or hundreds of data points, but the integrity of the process is paramount because the value of the scientific advice depends on that integrity.”

Empowered by data and collaboration, Dulvy’s interdisciplinary team must integrate data science, advanced research computing, modelling and data visualization with their ecological insights. “The joy of big data is that all of us tend to use the same statistical and GIS techniques. Even though in our wider Earth to Ocean research group there are 40 or 50 people, we all speak a common language of statistical programming,” he explains. “It doesn't matter whether you're working on flies, beetles, pollinating bees, salmon, or sharks. The joy is the tools are a common and having the common tools actually also means that we can answer any question we want to.”

“It doesn't matter whether you're working on flies, beetles, pollinating bees, salmon, or sharks. The joy is the tools are a common and having the common tools actually also means that we can answer any question we want to.”

Their insights are also informing better environmental policies that protect oceans, reefs, and inevitably save marine life. “I'm really excited because I get up in the morning and every single day we take a step forward to a more focused plan for what we're going to save. We're taking steps to save species. That's really exciting. We're moving from this problem-description phase to solution-generation phase.”

While big data may be helping save sharks, it’s also equipping the next generation of ecologists at SFU with the capabilities to create meaningful change. While the wonders of the natural world continue to stoke the curiosity of students and researchers, today’s ecologists must be equally adept inside spreadsheets and databases where huge volumes of data are being compiled and mined in real-time.

“Because big data is relatively new, most people come into biology because they want to be at the lab or they want to be in the field. They don't come into biology to sit at a computer screen,” says Dulvy. “What brings the right people to Earth to Ocean is that we're a lab that does marine science that matters and it's science that's socially and politically relevant.”

Many of his students are intimidated by data-intensive statistical analysis at first. But on a snowy winter morning at SFU, Dulvy and his students move fluidly between the dissecting tables — where they examine the size of a ray’s brain — to their collaboration room, where they analyze complex and colorful data visualizations on a large monitor. With Dulvy’s mentorship, students are advancing their knowledge and changing the field of ecology.